Research Engineer, Machine Learning (AI for Science)

We're partnering with one of Europe's most exciting AI-native startups that's building an autonomous AI platform to fundamentally reinvent how new materials are discovered.

Rather than relying on simulations alone, they're combining cutting-edge machine learning with a high-throughput experimental laboratory, creating a closed-loop system where AI designs new materials, experiments validate them, and real-world results continuously improve the models.

Backed by $60M from leading global investors, they've assembled an exceptional team spanning AI, physics, materials science and engineering, and are now looking for outstanding Machine Learning Research Engineers to help build the infrastructure powering the next generation of AI for Science.

You’ll be working on:

• Building scalable ML infrastructure for frontier AI research
• Translating novel research into production-quality ML systems
• Distributed training and inference across large GPU clusters
• Optimising model performance, training pipelines and experimentation workflows
• Developing multimodal data pipelines spanning simulations, laboratory data and scientific literature
• Working alongside world-class AI researchers, engineers and scientists to deploy models into a real-world autonomous experimentation platform
• Strong Machine Learning Engineering experience
• Deep knowledge of modern ML architectures (Transformers, GNNs, Diffusion Models, etc.)
• Excellent Python skills with PyTorch and/or JAX
• Experience building scalable production ML systems
• Someone who enjoys turning cutting-edge research into robust, high-performance software
• Distributed GPU training (multi-GPU / multi-node)
• Scientific machine learning or simulation environments
• Performance optimisation and systems engineering
• Docker, Kubernetes, GCP or similar infrastructure
• Scientific computing or research engineering backgrounds

Why this opportunity?

This is a chance to join an exceptionally well-funded AI-for-Science company at an early stage and help build technology capable of accelerating scientific discovery in areas that matter globally. You'll work alongside some of the world's leading researchers on genuinely novel problems, with significant technical ownership from day one.